arXiv subjects

Yiqi Huang

Publications and source records attributed to Yiqi Huang.

At least 19 recordsLinked to original sources

arXiv2026

Differentiable Routability-Driven Package Floorplanning with Pin Assignment

As advanced packaging technology evolves, increasing interconnect density in redistribution layers (RDLs) makes routability critical to package floorplanning. Meanwhile, power integrity requirements often reserve fan-in regions for the power delivery network (PDN), forcing signal nets through fan-out regions and complicating routability estimation. Existing uniform grid-based congestion models cannot accurately characterize fan-out congestion, while previous pin assignment methods struggle to evaluate net crossings. We propose a differentiable routability-driven floorplanning and pin assignment algorithm for advanced packaging with fan-out routing. First, a differentiable wirelength minimization method directly models discrete chip orientations and back-propagates wirelength gradients to chip locations and orientations. It reduces wirelength under fixed pin selection while avoiding the bias of continuous-angle modeling. Second, a crossing-aware pin assignment method incorporates net-crossing cost into a multi-strategy DPSO algorithm and uses GPU-parallel cost evaluation to reduce wirelength efficiently. Finally, a differentiable routability maximization method constructs a congestion model tailored to fan-out routing and establishes a back-propagation path from congestion information to chip locations, thereby guiding routability optimization. Experimental results show that our method achieves 100% routability on all benchmarks. For cases successfully routed by the baselines, it reduces wirelength by up to approximately 23% compared with a leading floorplanning method equipped with our pin assignment flow.

cs.AR↗

arXiv2026

Cosmological constraints from neighbor-density-weighted marked correlation functions

We investigate whether neighbor-density-weighted marked correlation functions (MCFs) can extract cosmological information beyond the standard redshift-space two-point correlation function (2PCF). Using the Kun suite of 129 $w_0w_a$CDM$+\sum m_ν$ simulations in $1~h^{-1}{\rm Gpc}$ boxes, we construct Gaussian-process emulators for the normalized scale statistic $\widehat{W}^α(s)$ and the angular statistic $\widehat{W}^α_{Δs}(μ)$. We perform joint analyses combining multiple mark parameters $α$ and quantify the information gain using the FoM in the $Ω_m$--$σ_8$ plane. Relative to the 2PCF case, three-mark combinations improve the FoM by factors of $1.7$--$2.5$, while five-mark combinations increase the gain to $1.9$--$2.4$, depending on the statistic and mark definition. We further compare density and normalized-gradient marks, finding that they are nearly redundant for isotropic statistics but complementary for angular statistics, where their combination improves the FoM by up to $43\%$. Tests of scale range and halo selection show that the marked statistics remain robust under changes in analysis choices, with the angular statistic retaining additional cosmological information that is less sensitive to tracer selection. Our results demonstrate that MCFs substantially enhance cosmological constraints beyond the standard 2PCF and provide a robust probe for next-generation galaxy surveys.

astro-ph.CO↗

arXiv2026

Regularity of Einstein 5-manifolds via 4-dimensional gap theorems

We refine the regularity of noncollapsed limits of 5-dimensional manifolds with bounded Ricci curvature. In particular, for noncollapsed limits of Einstein 5-manifolds, we prove that (1) tangent cones are unique of the form $\mathbb{R}\times\mathbb{R}^4/Γ$ on the top stratum, hence outside a countable set of points; this follows from a new isolation result for cones of the form $\mathbb{R}\times\mathbb{R}^4/Γ$ among all tangent cones, (2) the singular set is entirely contained in a countable union of Lipschitz curves and points, (3) away from a nowhere dense subset, these Lipschitz curves consist of smooth geodesics, (4) the interior of any geodesic is removable: limits of Einstein manifolds are real-analytic orbifolds with singularities along geodesic and bounded curvature away from their extreme points, and (5) if an asymptotically Ricci-flat 5-manifold with Euclidean volume growth has one tangent cone at infinity that splits off a line, then it is the unique tangent cone at infinity. These results prompt the question of the orbifold regularity of noncollapsed limits of Einstein manifolds off a codimension 5 set in arbitrary dimension. The proofs rely on a new result of independent interest: all spherical and hyperbolic 4-orbifolds are isolated among Einstein 4-orbifolds in the Gromov-Hausdorff sense. This yields various gap theorems for Einstein 4-orbifolds, which do not extend to higher dimensions. The proofs of these gap theorems require a careful analysis of singular metrics and families of metrics that degenerate.

math.DG↗

arXiv2026

Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising

Recently, the mainstream practice for training low-light raw image denoising methods has shifted towards employing synthetic data. Noise modeling, which focuses on characterizing the noise distribution of real-world sensors, profoundly influences the effectiveness and practicality of synthetic data. Currently, physics-based noise modeling struggles to characterize the entire real noise distribution, while learning-based noise modeling impractically depends on paired real data. In this paper, we propose a novel strategy: learning the noise model from dark frames instead of paired real data, to break down the data dependency. Based on this strategy, we introduce an efficient physics-informed noise neural proxy (PNNP) to approximate the real-world sensor noise model. Specifically, we integrate physical priors into neural proxies and introduce three efficient techniques: physics-guided noise decoupling (PND), physics-aware proxy model (PPM), and differentiable distribution loss (DDL). PND decouples the dark frame into different components and handles different levels of noise flexibly, which reduces the complexity of noise modeling. PPM incorporates physical priors to constrain the synthetic noise, which promotes the accuracy of noise modeling. DDL provides explicit and reliable supervision for noise distribution, which promotes the precision of noise modeling. PNNP exhibits powerful potential in characterizing the real noise distribution. Extensive experiments on public datasets demonstrate superior performance in practical low-light raw image denoising. The source code will be publicly available at the project homepage.

eess.IV↗

arXiv2025

Constraining $σ_8$ with Lensing Statistics in Low and High Density Regions

Lensing studies are typically carried out around high density regions, such as groups and clusters, where the lensing signals are significant and indicative of rich density structures. However, a more comprehensive test of the cosmological model should also include the lensing effect in low density regions. In this work, we incorporate the stacked weak lensing signals around the low density positions, alongside galaxy-galaxy lensing and galaxy-galaxy two point correlation function to perform a joint cosmological analysis on $σ_8$. The low density positions are constructed from the DR9 data release of the DESI legacy imaging survey, using galaxies with r-band absolute magnitude cut M$<$-21.5 and in the redshift range of 0.18$<$z$<$0.28. In doing so, we simultaneously parameterize photometric redshift errors and halo mass uncertainties while building mock catalogs from simulations using the method of SubHalo Abundance Matching (SHAM). For the weak lensing measurements, we use the shear estimators derived from the DECaLS DR8 imaging data, processed by the Fourier_Quad pipeline. The survey boundaries and masks are fully taken into account. Our analysis achieves a total significance of $31.1σ$ detection for lensing in the low density positions, which significantly improve the $σ_8$ constraint compared to galaxy-galaxy lensing and galaxy-galaxy two point correlation function by 14$\%$. For flat $Λ$CDM model, we constrain $σ_8$ =$0.824^{+0.015}_{-0.015}$, which shows a good agreement with the PLANCK result. Additionally, the halo mass uncertainty $σ_{\lg M}$ and photometric redshift error $σ_z$ are constrained to be $0.565^{+0.086}_{-0.070}$ and $0.004^{+0.004}_{-0.003}$ respectively, which are somewhat different from our expectations due to the significant degeneracy of the two parameters.

astro-ph.CO↗

arXiv2025

EBT-Policy: Energy Unlocks Emergent Physical Reasoning Capabilities

Implicit policies parameterized by generative models, such as Diffusion Policy, have become the standard for policy learning and Vision-Language-Action (VLA) models in robotics. However, these approaches often suffer from high computational cost, exposure bias, and unstable inference dynamics, which lead to divergence under distribution shifts. Energy-Based Models (EBMs) address these issues by learning energy landscapes end-to-end and modeling equilibrium dynamics, offering improved robustness and reduced exposure bias. Yet, policies parameterized by EBMs have historically struggled to scale effectively. Recent work on Energy-Based Transformers (EBTs) demonstrates the scalability of EBMs to high-dimensional spaces, but their potential for solving core challenges in physically embodied models remains underexplored. We introduce a new energy-based architecture, EBT-Policy, that solves core issues in robotic and real-world settings. Across simulated and real-world tasks, EBT-Policy consistently outperforms diffusion-based policies, while requiring less training and inference computation. Remarkably, on some tasks it converges within just two inference steps, a 50x reduction compared to Diffusion Policy's 100. Moreover, EBT-Policy exhibits emergent capabilities not seen in prior models, such as zero-shot recovery from failed action sequences using only behavior cloning and without explicit retry training. By leveraging its scalar energy for uncertainty-aware inference and dynamic compute allocation, EBT-Policy offers a promising path toward robust, generalizable robot behavior under distribution shifts.

cs.RO↗

arXiv2025

On the rate of convergence of cylindrical singularity in mean curvature flow

We prove that if a rescaled mean curvature flow is a global graph over the round cylinder with small gradient and converges super-exponentially fast, then it must coincide with the cylinder itself. We also show that the result is sharp with counter-examples of local graphs at arbitrarily super-exponential convergence rate with the domain expanding arbitrarily fast. The first part provides the first unique continuation result in the cylindrical setting, the generic singularity model in mean curvature flow. In sharp contrast, in the second part we construct infinite-dimensional families of Tikhonov-type examples for nonlinear equations, including the rescaled mean curvature flow, showing that unique continuation fails for local graphical solutions. These examples demonstrate the essential role of global graphical assumptions in rigidity and highlight new phenomena absent in the compact case. We also construct non-product mean curvature flows that develop singular sets as prescribed lower dimensional Euclidean space at arbitrary super-exponential rates. Our construction works in great generality for a large class of non-linear equations.

math.DG↗

arXiv2025

The Bounded Diameter Conjecture and Sharp Geometric Estimates for Mean Curvature Flow

We show that the intrinsic diameter of mean curvature flow in $\mathbb{R}^3$ is uniformly bounded as one approaches the first singular time $T$. This confirms the bounded diameter conjecture of Haslhofer. In addition, we establish several sharp quantitative estimates: the second fundamental form $A$ has uniformly bounded $L^1$-norm on each time slice, $A$ belongs to the weak $L^3$ space on the space-time region, and the singular set $\mathcal{S}$ has finite $\mathcal{H}^1$-Hausdorff measure. All of the results are optimal due to the marriage ring example and our results do not require any convexity assumptions on the surfaces. Furthermore, our arguments extend naturally to flows through singularities, yielding the same sharp estimates.

math.DG↗

arXiv2025

Tenma: Robust Cross-Embodiment Robot Manipulation with Diffusion Transformer

Scaling Transformer policies and diffusion models has advanced robotic manipulation, yet combining these techniques in lightweight, cross-embodiment learning settings remains challenging. We study design choices that most affect stability and performance for diffusion-transformer policies trained on heterogeneous, multimodal robot data, and introduce Tenma, a lightweight diffusion-transformer for bi-manual arm control. Tenma integrates multiview RGB, proprioception, and language via a cross-embodiment normalizer that maps disparate state/action spaces into a shared latent space; a Joint State-Time encoder for temporally aligned observation learning with inference speed boosts; and a diffusion action decoder optimized for training stability and learning capacity. Across benchmarks and under matched compute, Tenma achieves an average success rate of 88.95% in-distribution and maintains strong performance under object and scene shifts, substantially exceeding baseline policies whose best in-distribution average is 18.12%. Despite using moderate data scale, Tenma delivers robust manipulation and generalization, indicating the great potential for multimodal and cross-embodiment learning strategies for further augmenting the capacity of transformer-based imitation learning policies.

cs.RO↗

arXiv2025

YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency

The rapid advancement of photography has created a growing demand for a practical blind raw image denoising method. Recently, learning-based methods have become mainstream due to their excellent performance. However, most existing learning-based methods suffer from camera-specific data dependency, resulting in performance drops when applied to data from unknown cameras. To address this challenge, we introduce a novel blind raw image denoising method named YOND, which represents You Only Need a Denoiser. Trained solely on synthetic data, YOND can generalize robustly to noisy raw images captured by diverse unknown cameras. Specifically, we propose three key modules to guarantee the practicality of YOND: coarse-to-fine noise estimation (CNE), expectation-matched variance-stabilizing transform (EM-VST), and SNR-guided denoiser (SNR-Net). Firstly, we propose CNE to identify the camera noise characteristic, refining the estimated noise parameters based on the coarse denoised image. Secondly, we propose EM-VST to eliminate camera-specific data dependency, correcting the bias expectation of VST according to the noisy image. Finally, we propose SNR-Net to offer controllable raw image denoising, supporting adaptive adjustments and manual fine-tuning. Extensive experiments on unknown cameras, along with flexible solutions for challenging cases, demonstrate the superior practicality of our method. The source code will be publicly available at the \href{https://fenghansen.github.io/publication/YOND}{project homepage}.

cs.CV↗

arXiv2025

Spatial RoboGrasp: Generalized Robotic Grasping Control Policy

Achieving generalizable and precise robotic manipulation across diverse environments remains a critical challenge, largely due to limitations in spatial perception. While prior imitation-learning approaches have made progress, their reliance on raw RGB inputs and handcrafted features often leads to overfitting and poor 3D reasoning under varied lighting, occlusion, and object conditions. In this paper, we propose a unified framework that couples robust multimodal perception with reliable grasp prediction. Our architecture fuses domain-randomized augmentation, monocular depth estimation, and a depth-aware 6-DoF Grasp Prompt into a single spatial representation for downstream action planning. Conditioned on this encoding and a high-level task prompt, our diffusion-based policy yields precise action sequences, achieving up to 40% improvement in grasp success and 45% higher task success rates under environmental variation. These results demonstrate that spatially grounded perception, paired with diffusion-based imitation learning, offers a scalable and robust solution for general-purpose robotic grasping.

cs.RO↗

arXiv2025

RoboGrasp: A Universal Grasping Policy for Robust Robotic Control

Imitation learning and world models have shown significant promise in advancing generalizable robotic learning, with robotic grasping remaining a critical challenge for achieving precise manipulation. Existing methods often rely heavily on robot arm state data and RGB images, leading to overfitting to specific object shapes or positions. To address these limitations, we propose RoboGrasp, a universal grasping policy framework that integrates pretrained grasp detection models with robotic learning. By leveraging robust visual guidance from object detection and segmentation tasks, RoboGrasp significantly enhances grasp precision, stability, and generalizability, achieving up to 34% higher success rates in few-shot learning and grasping box prompt tasks. Built on diffusion-based methods, RoboGrasp is adaptable to various robotic learning paradigms, enabling precise and reliable manipulation across diverse and complex scenarios. This framework represents a scalable and versatile solution for tackling real-world challenges in robotic grasping.

cs.RO↗

arXiv2024

Spatially Visual Perception for End-to-End Robotic Learning

Recent advances in imitation learning have shown significant promise for robotic control and embodied intelligence. However, achieving robust generalization across diverse mounted camera observations remains a critical challenge. In this paper, we introduce a video-based spatial perception framework that leverages 3D spatial representations to address environmental variability, with a focus on handling lighting changes. Our approach integrates a novel image augmentation technique, AugBlender, with a state-of-the-art monocular depth estimation model trained on internet-scale data. Together, these components form a cohesive system designed to enhance robustness and adaptability in dynamic scenarios. Our results demonstrate that our approach significantly boosts the success rate across diverse camera exposures, where previous models experience performance collapse. Our findings highlight the potential of video-based spatial perception models in advancing robustness for end-to-end robotic learning, paving the way for scalable, low-cost solutions in embodied intelligence.

cs.CV↗

arXiv2024

The Nodal Sets of Solutions to Parabolic Equations

In this paper, we study the parabolic equations $\partial_t u=\partial_j\left(a^{ij}(x,t)\partial_iu\right)+b^j(x,t)\partial_ju+c(x,t)u$ in a domain of $\mathbb{R}^n$ under the condition that $a^{ij}$ are Lipschitz continuous. Consider the nodal set $Z_t=\{x: u(x,t)=0\}$ at a time $t$-slice. Simple examples show that the singular set $\mathcal{S}_t=\{x: u(x,t)=|\nabla_x u|(x,t)=0\}$ may coincide with nodal set. This makes the methods used in the study of nodal sets for elliptic equations fail, rendering the parabolic case much more complicated. The current strongest results in the literature establish the finiteness of the $(n-1)$-dimensional Hausdorff measure of $Z_t$, assuming either $n=1$ by Angenent or that the coefficients are time-independent and analytic by Lin. With general coefficients, the codimension-one estimate was obtained under some doubling assumption by Han-Lin but only for space-time nodal sets. In the first part, we prove that $\mathcal{H}^{n-1}(Z_t) < \infty$ in full generality, i.e. for any dimension, with time-dependent coefficients and with merely Lipschitz regular leading coefficients $a^{ij}$. In the second part, we study the evolutionary behavior of nodal sets. When $n=1$, it is proved by Angenent that the number of nodal points is non-increasing in time. For the $n$-dimensional case, we construct examples showing that measure monotonicity fails. In contrast, we prove dimension monotonicity, i.e., the Hausdorff dimension of the nodal set is non-increasing in time. This is the first monotonicity property for nodal sets in general dimensions. All the assumptions here are sharp.

math.DG↗

arXiv2023

Volume Estimates for Singular sets and Critical Sets of Elliptic Equations with Hölder Coefficients

Consider the solutions $u$ to the elliptic equation $\mathcal{L}(u) = \partial_i(a^{ij}(x) \partial_j u) + b^i(x) \partial_i u + c(x) u= 0$ with $a^{ij}$ assumed only to be Hölder continuous. In this paper we prove an explicit bound for $(n-2)$-dimensional Minkowski estimates of singular set $\mathcal{S}(u) = \{ x \in B_1 : u(x) = |\nabla u(x)| = 0\}$ and critical set $\mathcal{C}(u) \equiv \{ x\in B_{1} : |\nabla u(x)| = 0 \}$ in terms of the bound on doubling index, depending on $c \equiv 0$ or not. Here the Hölder assumption is sharp as it is the weakest condition in order to define the critical set of $u$ according to elliptic estimates. We can also obtain an optimal improvement on Cheeger-Naber-Valtorta's volume estimates on each quantitative stratum $\mathcal{S}^k_{η, r}$. The main difficulty in this situation is the lack of monotonicity formula which is essential to the quantitative stratification. In our proof, one key ingredient is a new almost monotonicity formula for doubling index under the Hölder assumption. Another key ingredient is the quantitative uniqueness of tangent maps. It deserves to note that our almost monotonicity is sufficient to address all the difficulties arising from the absence of monotonicity in the analysis of differential equations. We believe the idea could be applied to other relevant study.

math.AP↗

arXiv2022

Scalar curvature lower bound under integral convergence

In this work, we consider sequences of $C^2$ metrics which converge to a $C^2$ metric in $C^0$ sense. We show that if the scalar curvature of the sequence is almost non-negative in the integral sense, then the limiting metric has scalar curvature lower bound in point-wise sense.

math.DG↗

arXiv2022

Halo Properties and Mass Functions of Groups/Clusters from the DESI Legacy Imaging Surveys DR9

Based on a large group/cluster catalog recently constructed from the DESI Legacy Imaging Surveys DR9 using an extended halo-based group finder, we measure and model the group-galaxy weak lensing signals for groups/clusters in a few redshift bins within redshift range $0.1 \leqslant z<0.6$. Here, the background shear signals are obtained based on the DECaLS survey shape catalog derived with the \textsc{Fourier\_Quad} method. We divide the lens samples into 5 equispaced redshift bins and 7 mass bins, which allow us to probe the redshift and mass dependence of the lensing signals and hence the resulting halo properties. In addition to these sample selections, we have also checked the signals around different group centers, e.g., brightest central galaxy (BCG), luminosity weighted center and number weighted center. We use a lensing model that includes off-centering to describe the lensing signals we measure for all mass and redshift bins. The results demonstrate that our model predictions for the halo masses, bias and concentrations are stable and self-consistent among different samples for different group centers. Taking advantage of the very large and complete sample of groups/clusters, as well as the reliable estimation of their halo masses, we provide measurements of the cumulative halo mass functions up to redshift $z=0.6$, with a mass precision at $0.03\sim0.09$ dex.

astro-ph.CO↗

arXiv2022

N-body simulations, halo mass functions, and halo density profile in $f(T)$ gravity

We perform N-body simulations for $f(T)$ gravity using the ME-Gadget code, in order to investigate for the first time the structure formation process in detail. Focusing on the power-law model, and considering the model-parameter to be consistent within 1$σ$ with all other cosmological datasets (such as SNIa, BAO, CMB, CC), we show that there are clear observational differences between $Λ$CDM cosmology and $f(T)$ gravity, due to the modifications brought about the latter in the Hubble function evolution and the effective $Newton\prime s$ constant. We extract the matter density distribution, matter power spectrum, counts-in-cells, halo mass function and excess surface density (ESD) around low density positions (LDPs) at present time. Concerning the matter power spectrum we find a difference from $Λ$CDM scenario, which is attributed to about 2/3 to the different expansion and to about 1/3 to the effective gravitational constant. Additionally, we find a difference in the cells, which is significantly larger than the Poisson error, which may be distinguishable with weak-lensing reconstructed mass maps. Moreover, we show that there are different massive halos with mass $M>10^{14}M_{\odot}/h$, which may be distinguishable with statistical measurements of cluster number counting, and we find that the ESD around LDPs is mildly different. In conclusion, high-lighting possible smoking guns, we show that large scale structure can indeed lead us to distinguish General Relativity and $Λ$CDM cosmology from $f(T)$ gravity.

astro-ph.CO↗